Telecommunications
Use Ontologies to Optimize Networks, Prevent Revenue Loss, and Automate Operations
The Problem
Drowning in Fragmented Data
Network topology, subscriber records, billing systems, OSS, and BSS all live in separate silos. Engineers lack a unified view to correlate network events with customer impact, making root cause analysis slow and incident response reactive.
Reactive Operations
Without connected intelligence across network, service, and customer data, operations teams fight fires rather than prevent them. Cascading failures propagate undetected, SLA breaches occur before anyone can intervene, and troubleshooting requires days of manual correlation.
Lost Revenue & Poor Experience
Fraud goes undetected across subscriber and billing graphs, churn signals are buried in disconnected data, and 5G monetization opportunities remain unrealized because the infrastructure to connect network performance with customer value simply doesn't exist.
The Perfect Ontology Solution
Why It Matters Now
- 5G rollout and network slicing demand real-time intelligence across topology, performance, and subscriber data
- Revenue assurance is under pressure — telco fraud costs the industry billions annually and existing detection is too slow
- Customer expectations for always-on connectivity make proactive incident management a competitive necessity
- Agentic AI workflows are transforming network operations — but only for operators with connected, queryable infrastructure
- Regulatory and SLA obligations require traceable, explainable decisions across complex multi-layer network architectures
Solution & Features
- Unify OSS, BSS, network topology, and subscriber data in a single executable ontology
- Build queryable ontologies for automated incident detection and root cause analysis
- Automate network operations workflows — from fault detection to resolution — with agentic AI
- Enable agents to query ontologies in natural language across topology, congestion, and experience data
- Map subscriber, device, and billing relationships to detect fraud patterns and revenue leakage
- Connect structured network telemetry with unstructured ticket and log data for full context
- Graph-level analytics without graph database infrastructure — run on existing Snowflake or Databricks
Use Cases
- Cluster-wide failure analysis — identify root cause across hundreds of network nodes in minutes
- Natural language queries across congestion, topology, and customer experience data
- Agentic workflows for automated fault detection, triage, and resolution
- Fraud pattern mapping across subscriber, device, and billing graphs
- Digital twin simulations for network capacity planning and 5G slice optimisation
- Churn prediction by connecting network quality signals with customer behaviour
- 5G slice performance monitoring with SLA breach prediction and automated response
Benefits
- Automate repetitive network operations workflows and free engineers for higher-value work
- Root cause analysis in minutes — not days — across complex multi-layer network topologies
- Proactive issue resolution before customers are impacted, improving NPS and reducing churn
- Detect fraud patterns across subscriber and billing graphs faster than rule-based systems
- Build agentic network operations workflows on connected, queryable infrastructure
- Query complex network relationships in natural language — no specialist graph query skills required
- No migration — connect to existing OSS, BSS, and data platform infrastructure immediately
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Resource Center
Perspectives on our customers, future of data, AI, and ontologies.
A case study on how the Sella Group uses Prometheux to power credit risk at enterprise scale.
A whitepaper discussing the role of executable ontologies in enhancing enterprise security.
